Ghost Imaging Using Orbital Angular Momentum

Zhao, Shengmei; Ding, Jianping; Xiang, Dong; Zheng, Baoyu

doi:10.1088/0256-307x/28/12/124207

Cited by 10 publications

(7 citation statements)

References 23 publications

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“…The LG modes are the easiest type of vortex beams to generate in a laboratory setting, and excellent reviews exist on the theoretical explanation of OAM, generation of LG beams, and applications using LG beams [36,37]. An extensive study of the vortex beams has found ample applications in the field of communications and optical imaging, among others [35,[38][39][40][41][42][43][44][45][46]. Compared to the traditional Gaussian beam, the use of vortex beams in an imaging system shows significant improvement in the image quality.…”

Section: Orbital Angular Momentum Of Light and Imaging Using Vortex Bmentioning

confidence: 99%

Detecting objects behind scattering media using vortex beams and deep learning

Balasubramaniam

Biton

Arnon

2021

Preprint

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Reconstructing objects behind scattering media is a challenging issue with applications in biomedical imaging, non-distractive testing, computer-assisted surgery, and autonomous vehicular systems. Such systems’ main challenge is the multiple scattering of the photons in the angular and spatial domain, which results in a blurred image. Previous works try to improve the reconstructing ability using deep learning algorithms, with some success. We enhance these methods by illuminating the set-up using several modes of vortex beams obtaining a series of time-gated images corresponding to each mode. The images are accurately reconstructed using a deep learning algorithm by analyzing the pattern captured in the camera. This study shows that using vortex beams instead of Gaussian beams enhances the deep learning algorithm’s image reconstruction ability in terms of the peak signal-to-noise ratio (PSNR) by ~ 2.5 dB and ~1 dB when low and high scattering scatterers are used respectively.

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Section: Orbital Angular Momentum Of Light and Imaging Using Vortex Bmentioning

confidence: 99%

Detecting objects behind scattering media using vortex beams and deep learning

Balasubramaniam

Biton

Arnon

2021

Preprint

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“…Ghost imaging (GI), also called coincidence imaging or correlated imaging, has attracted much attention recently. [1][2][3][4][5][6][7][8][9][10] In a GI system, the image of an object is retrieved by using two spatially correlated beams, a signal beam and a reference beam. The signal beam after illuminating an object is collected with a bucket detector with no spatial resolution.…”

Section: Introductionmentioning

confidence: 99%

Correspondence normalized ghost imaging on compressive sensing

Zhao¹,

Zhuang²

2014

Chinese Phys. B

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Ghost imaging (GI) offers great potential with respect to conventional imaging techniques. It is an open problem in GI systems that a long acquisition time is be required for reconstructing images with good visibility and signal-to-noise ratios (SNRs). In this paper, we propose a new scheme to get good performance with a shorter construction time. We call it correspondence normalized ghost imaging based on compressive sensing (CCNGI). In the scheme, we enhance the signal-to-noise performance by normalizing the reference beam intensity to eliminate the noise caused by laser power fluctuations, and reduce the reconstruction time by using both compressive sensing (CS) and time-correspondence imaging (CI) techniques. It is shown that the qualities of the images have been improved and the reconstruction time has been reduced using CCNGI scheme. For the two-grayscale "double-slit" image, the mean square error (MSE) by GI and the normalized GI (NGI) schemes with the measurement number of 5000 are 0.237 and 0.164, respectively, and that is 0.021 by CCNGI scheme with 2500 measurements. For the eight-grayscale "lena" object, the peak signal-to-noise rates (PSNRs) are 10.506 and 13.098, respectively using GI and NGI schemes while the value turns to 16.198 using CCNGI scheme. The results also show that a high-fidelity GI reconstruction has been achieved using only 44% of the number of measurements corresponding to the Nyquist limit for the two-grayscale "double-slit" object. The qualities of the reconstructed images using CCNGI are almost the same as those from GI via sparsity constraints (GISC) with a shorter reconstruction time.

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“…Compared with QGI without OAM, HGI has some advantages. HGI provides a convenient method for GI system with phase objects, to enhance the edge contrast of image, to need no background subtraction for the high contrast, to provide the potential to implement GI for multiple grayscale objects [12], [13]. HGI is more severely damaged by turbulence aberration [14] because OAM is encoded in the spatial profile of light beams [15]- [17].…”

Section: Introductionmentioning

confidence: 99%

Improving the Atmosphere Turbulence Tolerance in Holographic Ghost Imaging System by Channel Coding

Zhao

Wang

Gong

et al. 2013

J. Lightwave Technol.

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Atmospheric turbulence (AT) severely cuts down the image resolutions of holographic ghost imaging (HGI) since orbital angular momentum (OAM) entangled states are associated with the spatial distribution of wave-functions. In this paper, we investigate the method to improve AT tolerance in HGI system. Since Reed-Solomon (RS) codes are important non-binary error correction codes which have strong correcting ability against noise, we use RS codes to develop a RS-coded HGI scheme. In order to verify the improvement quantitatively, we compute and compare the Peak Signal-to-Noise ratio (PSNR) of the reconstructed image in HGI without and with RS codes. The numerical results show that PSNRs go down quickly with the increase of the strength of turbulence, and they are improved greatly by using RS codes. For different grayscale objects, the results show that the improvement by RS codes is enhanced with the increasing correction ability, but it does not work for the same object by enlarging its grayscale to suit for more errors.

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Ghost Imaging Using Orbital Angular Momentum

Cited by 10 publications

References 23 publications

Detecting objects behind scattering media using vortex beams and deep learning

Detecting objects behind scattering media using vortex beams and deep learning

Correspondence normalized ghost imaging on compressive sensing

Improving the Atmosphere Turbulence Tolerance in Holographic Ghost Imaging System by Channel Coding

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